2.6. Dependencies

2.6.1. modules

You can install modules compiled and provided by the HPC team. See Chapter 4.1.

2.6.2. pip

You can save Python dependencies in a requirements.txt file and install them (https://pip.pypa.io/en/stable/user_guide/#requirements-files).

2.6.3. R

For Bioinformatics, you can install using BiocManager. This is mostly done at the top of your main script.

2.6.4. conda

https://conda.io/projects/conda/en/latest/user-guide/install/linux.html

You can use env.yaml file to list your dependencies an update them.

name: myenv
channels:
    - pytorch
    - nvidia
    - conda-forge
dependencies:
    - python<3.11
    - pytorch
    - torchvision
    - pytorch-cuda=11.7
    - pip
    - pip:
        - pip_only_package

conda env update  --file env.yaml --prune

The latest version of conda can use the libmamba solver, greatly speeding up dependy solving. (https://www.anaconda.com/blog/conda-is-fast-now)

conda update -n base conda
conda install -n base conda-libmamba-solver
conda config --set solver libmamba

2.6.5. mamba

Mamba is a faster version of conda, although with libmamba in conda, it’s usage is now more limited.

conda-forge/miniforge

curl -L -O “conda-forge/miniforge\((uname)-\)(uname -m).sh” bash Mambaforge-\((uname)-\)(uname -m).sh

As installation path, use $VSC_DATA_VO_USER/mambaforge as it has much more space available. Execute the conda init command given by the installer. Add the following lines to your .bashrc:

CONDA_ENVS_PATH=\(VSC_DATA_VO_USER/mambaforge/envs CONDA_PKGS_PATH=\)VSC_DATA_VO_USER/mambaforge/pkgs